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        "<h3>Predict secondary structure 3 states using ProtBert-BFD model</h3>\n",
        "<h4>You can make free prediction online through this \n",
        "<a href=\"https://huggingface.co/Rostlab/prot_bert_bfd_ss3?text=N+L+Y+I+Q+W+L+K+D+G+G+P+S+S+G+R+P+P+P+S\">Link</a></h4>"
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      "source": [
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        "colab": {
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      },
      "source": [
        "!pip install -q transformers"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 1.1MB 2.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 17.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.0MB 20.0MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 44.3MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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    {
      "cell_type": "code",
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        "id": "DzGbyjvbjdIs"
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        "from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline\n",
        "import re"
      ],
      "execution_count": 2,
      "outputs": []
    },
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            "3f3d4e8d95ef43cb911b2a25a861309a"
          ]
        }
      },
      "source": [
        "pipeline = TokenClassificationPipeline(\n",
        "    model=AutoModelForTokenClassification.from_pretrained(\"Rostlab/prot_bert_bfd_ss3\"),\n",
        "    tokenizer=AutoTokenizer.from_pretrained(\"Rostlab/prot_bert_bfd_ss3\", skip_special_tokens=True),\n",
        "    device=0\n",
        ")"
      ],
      "execution_count": 3,
      "outputs": [
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=769.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
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        {
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          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1676081643.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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            "tags": []
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          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "model_id": "840a6ec6db9f42c3b84bea4fca3d9ed9",
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        },
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            "\n"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r812U7GwnSXF"
      },
      "source": [
        "**3. Create or load sequences and map rarely occured amino acids (U,Z,O,B) to (X)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOaEESGWkXJu"
      },
      "source": [
        "sequences_Example = [\"M G A E E E D T A I L Y P F T I S G N D R N G N F T I N F K G T P N S T N N G C I G Y S Y N G D W E K I E W E G S C D G N G N L V V E V P M S K I P A G V T S G E I Q I W W H S G D L K M T D Y K A L E H H H H H H\",\n",
        "                     \"M N K Y L F E L P Y E R S E P G W T I R S Y F D L M Y N E N R F L D A V E N I V N K E S Y I L D G I Y C N F P D M N S Y D E S E H F E G V E F A V G Y P P D E D D I V I V S E E T C F E Y V R L A C E K Y L Q L H P E D T E K V N K L L S K I P S A G H H H H H H\"]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkZLVmYRkZ6k"
      },
      "source": [
        "sequences_Example = [re.sub(r\"[UZOB]\", \"X\", sequence) for sequence in sequences_Example]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-aGAcDhnZKF"
      },
      "source": [
        "**4. Make Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noEGrN0hkbJ6",
        "outputId": "b3291e69-4708-4366-b513-3d71743b1ae3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pipeline(sequences_Example)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
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